The high resolution aeromagnetic data that was used for the study consist of sixteen (16) sheets of aeromagnetic maps of total field intensity in half degree sheets obtained from Nigerian Geological Survey Agency (NGSA) [12]. The data was acquired by Fugro Airborne Survey Limited, as part of a programme aimed at assisting and promoting mineral exploration in Nigeria using 3× Scintrex CS3 Cesium Vapour Magnetometer with Flight Line Spacing of 500 meter, Flight Line Direction NW-SE, Terrain Clearance 80 meters, Tie Line Spacing 2 kilometres.

Data was processed using various filters on the Aeromagnetic data which revealed certain features that aid the interpretation. Thus the digitized data was imported into the computer to produce the total Magnetic intensity maps. The Total Magnetic data was further subjected to Polynomial filtering to obtain both the Regional and the Residual maps. Spectral analysis was used to determine average sediment thickness. All these were carried out using the Oasis montaj, Math lab, and ArcGIS software.

In polynomial fitting the regional is matched with mathematical Polynomial of low order to expose the residual features as random errors, and the treatment is based on statistical theory. The observed data are used to compute, usually by least square method, the mathematically described surface given the closet fit to the magnetic field that can be obtained within a specified degree of detail. This surface is considered to be the regional field and the residual is the difference between the magnetic field values thus determined [13]. The simplest approach is to fit a polynomial of first order to the magnetic data over a large area as possible around the zone of interest and to subtract the polynomial surface from the observed surface. If the regional field were a simple inclined plane it will be a first order surface. Thus

Z = A x + B y + C (1)

The next stage of complexity is the representation of a second order polynomial where,

Z = A x 2 + B y 2 + C x y D x + E y + F (2)

The residual magnetic field of the study area was produced by subtracting the regional field from the total magnetic field using the Polynomial fitting method. The computer program aero-super map was used to generate the coordinates of the total intensity field data values. This super data file, for all the magnetic values was used for production of composite aeromagnetic map of the study area using Oasis Montaj software version 7.0.1 [14]. The program was used to derive the residual magnetic values by subtracting values of regional field from the total magnetic field values to produce the residual magnetic map and the regional map.

There are different methods that are used for depth calculation, such as the inversion methods based on Parker’s forwarding calculation technique [15]. But the methods requires manual matching of anomalies to curve-types, which is time consuming and is restricted to the anomaly types for which curves exist, unlike the spectral method [16]. Developed a depth determination method which matches two dimensional power spectral calculated from gridded total magnetic intensity field data with corresponding spectral obtained from a theoretical model. For the purpose of analyzing aeromagnetic data, the ground is assumed to consist of a number of independent ensembles of rectangular, vertical sided parallelepiped, and each is ensemble characterized by a joint frequency distribution for the depth (h) and length (b) and depth extent (t).

In this work, the characteristics of the residual magnetic field was studied using statistical spectral method. This was done by first transforming the data from space to the frequency domain and then analyzing their frequency characteristics. In the general case, the radial spectrum may be conveniently approximated by straight line segments, the slopes of which relate to depths of the possible layers, [16] and [17]. The residual total magnetic field intensity values are used to obtain the two dimensional Fourier Transform, from which the spectrum is to be extracted from the residual values T ( X , Y ) consisting of M rows and N columns in X − Y . The evaluation is done using an algorithm that is a two dimensional extension of the fast Fourier transform [18]. Next, the frequency intervals are subdivided into sub-intervals, which lie within one unit of frequency range. The average spectrum of the partial values together constitutes the radial spectrum of the anomalous field [17], and [13].

The logarithm of the energy values versus frequency on a linear scale was plotted and the linear segments located the truncation effect (or Gibbs phenomenon). Three or two linear segments could be seen from the graphs. The first points on the frequency scale was ignored because the low frequency components in the energy spectrum are generated from the deepest layers whose locations are most likely in errors, each linear segment groups points due to anomalies caused by bodies occurring within a particular depth. If z is the mean depth of the layer, the depth factor for this ensemble of anomalies is exp(−2zk). Thus the logarithmic plot of the radial spectrum would give a straight line whose slope is −2z.

The mean depth of the burial ensemble is thus given as

Z = − m 2 (3)

where (m) is the slope of the best fitting straight line. Equation (3) can be applied directly if the frequency unit is in radian per kilometer. If however, the frequency unit is in circle per kilometer, the corresponding relationship can be expressed as

Z = − m 4 π (4)

In this study the aeromagnetic data set was divided into a block of 7.5' × 7.5' (13.73 km × 13.73 km) data points totaling 252 blocks excluding the Niger Republic part, which was subjected to Fast Fourier transformation (FFT) to compute the power spectrum of the magnetic data using Oasis montaj software.

From the spectral analysis (Figure 7) various depths of D1, the summary of the result of the depths is presented in Table 1. A careful observation of the D1 map revealed Sub-Basins based on the various depths that were observed in the area. These depths are similar to ones obtained by [10] and [4]. The coloured areas on Figure 7 are regarded as sub-basins because of their depth and thus high sediment thickness from 1.5 km and above. These are the possible potential areas for further hydrocarbon exploration. Though the result of the shallow Magnetic source depth (D2) shows that the basin is shallow with few identified areas of considerable depth indicating probable near surface igneous intrusions in the basin [10].

The major sub-basins identified include: sub-basin 1, with an approximate width of 43 km and length of 71 km; sub-basin 2, with an approximate width of 29 km and length of 31 km; sub-basin 3, with an approximate width of 20 km and length of 41 km; sub-basin 4, approximately 32 km in width and 39 km in length; sub-basin 5, with an approximate width of 19 km and length of 45 km; sub-basin 8, with an approximate width of 7 km and length of 40 km.

The result of the contoured deeper magnetic source depth (D1) was super imposed on the geologic map (Figure 9) to further observe the relationship of the sediments thickness and geology of the area. This shows that the sediment over burden are of shallow depth in the basement compared to the sedimentary area. Most of the areas with high sediment thickness were observed on the Gwandu Formation. The shales of Dange, Kalambaina and Dukamaje Formations could be the source rocks, while grits and sandstones of Taloka and Wurno Formations could serve as the reservoir rock.

The following conclusions can be drawn from this study:

1) Spectral analysis indicates that the basin is characterized by shallow and deeper sediments thickness.

2) Areas with shallow thickness of sediments, could not allow thermal maturation of the sediments, since temperature increase with depth. Depth of 2 km and above has a temperature range of 60˚C and above. The oil window according to [1] varies between 60˚C and 120˚C, above the temperature of 120˚C the oil may be thermally cracked to gas, and bellow 60˚C, it will form kerogen. Therefore areas with sediment thickness from 2 km and above could be good potential sites for hydrocarbon exploration.

The study area if given much attention, will add to the economic growth of Nigeria based on the hydrocarbon potentials revealed by this study. Therefore the use of other geophysical methods such as seismic and well logging is highly useful.